Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:271-287, 2019.
Reducing patients’ medical wait times by improving resource and staffing allocation is an important area of focus in hospital operations management. Two ways to decrease wait times are to adjust staffing or to limit the number of non-urgent visits to reflect a predicted volume of sick patients. Currently, this problem has been approached by both generalized linear models and time series models, and has mainly been researched in the context of adult emergency departments. We analyze sick visit data over a nine year period from one pediatric group (PG) that serves over 30,000 sick infants, children, and adolescents yearly in a walk-in and appointment-based out-patient clinic. The PG currently schedules staff and well-child appointments assuming a constant number of sick visits daily despite weekly and seasonal cycles in the data. We develop time series models to estimate the volume of sick patients that the PG can expect on any given day, so that clinicians can be allocated and the number of well-child appointments scheduled in advance can be adjusted according to predictions. First, we find that recurrent neural network (RNN) models are able to capture the seasonality of the data and perform substantially better than state-of-the-art models, including constant predictions. Next, we find that previous days’ data can be used to perform outbreak detection by identifying error outliers. Lastly, we find improvements in prediction when modeling sick patients as a mixture of disease types, because disease types are concentrated differently throughout the year. Resource allocation based on these findings can be expanded upon to reduce wait time by improving staffing at pediatric emergency departments and outpatient clinics.